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> ### Jonathan D. Klingler, Gary E. Hollibaugh, Jr., and Adam J. Ramey
> ### "Don't Know What You Got: A Bayesian Hierarchical Model of Neuroticism and Ideological Uncertainty
> ### Political Science Research and Methods
> ###
> ###
> ### Hierarchical Model Estimation Code
> ### Description: This file (one of eight) sets up the hierarchical model on a cluster node.
> ###
> ### Note 1: Run the Preprocessing_and_Summary.R file beforehand to generate the CCES2014.Rda file.
> ### Note 2: This file ends in SingleCoreXXX.R, and generates a file called DKWYGCoreXXX.Rda, where XXX is a number from 1-8.
> ### Note 3: Make sure the directories are set up properly and that all files are in this file's directory.
> ### Note 4: For ease of use, template script files called DKWYGCoreXXX.sh are included in the "Cluster Scripts" directory.
> ### Note 5: This file will start a process that will take several days to complete, so a cluster is recommended.
>
>
> rm(list=ls())
> ptm <- proc.time()
> 
> library(foreign)
> library(rjags)
Loading required package: coda
Linked to JAGS 3.4.0
Loaded modules: basemod,bugs
> library(grDevices)
> library(MASS)
> library(random)
> 
> 
> 
> load("CCES2014.Rda")
> 
> 
> # need to remove datapoints where questions were not asked
> # and where personality not elicited
> cces <- cces[!is.na(cces$self_emoti) & !is.na(cces$self_consc) & !is.na(cces$self_agree) 
+              & !is.na(cces$self_extra) & !is.na(cces$self_openn) & !is.na(cces$CC421a) 
+              & !is.na(cces$newsint) 
+              & !(cces$CC334C == "Not Asked") 
+              & !(cces$CC334D == "Not Asked") 
+              & !(cces$CC334E == "Not Asked") 
+              & !(cces$CC334F == "Not Asked") 
+              & !(cces$CC334G == "Not Asked") 
+              & !(cces$CC334K == "Not Asked") 
+              & !(cces$CC334L == "Not Asked") 
+              & !(cces$CC334M == "Not Asked") 
+              & !(cces$CC334W == "Not Asked"),]
> 
> attach(cces)
> 
> ##the dependent variable
> obama <- CC334C
> clinton <- CC334D
> cruz <- CC334E
> paul <- CC334F
> bush <- CC334G
> dem <- CC334K
> rep <- CC334L
> teaparty <- CC334M 
> scotus <- CC334W
> 
> y <- cbind(obama,cruz,clinton,paul,bush,dem,rep,teaparty,scotus)
> for (i in 1:ncol(y)) y[,i] <- as.numeric(y[,i])
> 
> y[y>=8] <- 0
> y[is.na(y)] <- 0
> y <- y+1
> 
> ##covariates for decisiveness and the saliency intercepts
> self_neuro <- 8 - self_emoti
> self_openn <- (self_openn - 1)/6
> self_consc <- (self_consc - 1)/6
> self_extra <- (self_extra - 1)/6
> self_agree <- (self_agree - 1)/6
> self_neuro <- (self_neuro - 1)/6
> polinterest <- (newsint=="Most of the time")*1
> unkinterest <- (newsint=="Don't know")*1
> faminc_recode <- faminc
> faminc_recode[which(faminc_recode == "$150,000 - $199,999")] <- levels(faminc_recode)[17]
> faminc_recode[which(faminc_recode == "$200,000 - $249,999")] <- levels(faminc_recode)[17]
> faminc_recode[which(faminc_recode == "$250,000 - $349,999")] <- levels(faminc_recode)[17]
> faminc_recode[which(faminc_recode == "$350,000 - $499,999")] <- levels(faminc_recode)[17]
> faminc_recode[which(faminc_recode == "$500,000 or more")] <- levels(faminc_recode)[17]
> faminc_recode[which(faminc_recode == "$250,000 or more ")] <- levels(faminc_recode)[17]
> income <- factor(faminc_recode)
> income_notsay <- (faminc_recode == "Prefer not to say")*1
> age <- 2014 - birthyr
> age2 <- (age^2)/100
> black <- (race == "Black")*1
> hisp <- (race == "Hispanic")*1
> other <- (race == "Asian" | race == "Native American" | race == "Mixed" | race == "Other" | race == "Middle Eastern")*1
> fulltime <- (employ == "Full-time")*1
> parttime <- (employ == "Part-time")*1
> unemploy <- (employ == "Unemployed")*1
> retired <- (employ == "Retired")*1
> female <- I(gender == "Female")*1
> education <- as.numeric(educ)
> 
> X <- cbind(1,
+            self_openn,
+            self_consc,
+            self_extra,
+            self_agree,
+            self_neuro,
+            female,
+            age,
+            age2,
+            black,
+            hisp,
+            other,
+            education,
+            polinterest,
+            unkinterest,
+            income,
+            income_notsay,
+            fulltime,
+            parttime,
+            unemploy,
+            retired)
> 
> ##covariate for saliency regression (equal to 1 if respondent and stimulus are the same party)
> Xs <- cbind((CC421a=="Democrat")*1, (CC421a=="Republican")*1, 
+             (CC421a=="Democrat")*1, (CC421a=="Republican")*1,
+             (CC421a=="Republican")*1, (CC421a=="Democrat")*1,
+             (CC421a=="Republican")*1, (CC421a=="Republican")*1,
+             (CC421a=="Republican")*1)
> 
> forJags <- list(y=y, x=X, Xs=Xs)
> 
> forJags$J <- nrow(y)
> forJags$K <- ncol(y)
> forJags$R <- ncol(X)
> forJags$nCat <- length(table(y))
> 
> ## priors
> forJags$b0 <- rep(0,ncol(X))
> forJags$B0 <- diag(.25,ncol(X))
> 
> ## initial values
> inits <- function(){list(beta_e=rep(0,ncol(X)),
+                    beta_d=rep(0,ncol(X)),
+                    beta_a=rep(0,ncol(X)),
+                    beta_s=0,
+                    sig_e=1,
+                    sig_a=1,
+                    tau0=seq(1,2,length=(forJags$nCat-3)))}
> 
> 
> n.tune <- 10000
> n.burnin <- 40000
> n.saved <- 50000
> n.thin <- 1
> 
> 
> set.seed(10000)
> temp.model <- jags.model(file="MLV2.bug",
+                          inits=inits,
+                          data=forJags,
+                          n.chains = 1,
+                          n.adapt = n.tune)
Compiling model graph
   Resolving undeclared variables
   Allocating nodes
   Graph Size: 277330

Initializing model

> update(temp.model, n.iter = n.burnin)
> dkwyg.7 <- coda.samples(temp.model,
+                         variable.names=c("beta_s","tau","beta_d","beta_e",
+                                          "beta_a","beta_b","gamma"),
+                         n.iter = n.saved,
+                         thin = n.thin)
> 
> save(dkwyg.7, file="DKWYGCore7.Rda")
> detach(cces)
> proc.time() - ptm
      user     system    elapsed 
259470.075      2.419 259527.505 
> 
> 
> proc.time()
      user     system    elapsed 
259479.847      2.933 259537.782 
